Currently accepted at: Journal of Medical Internet Research
Date Submitted: Jun 5, 2024
Date Accepted: Nov 19, 2024
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/62941
The final accepted version (not copyedited yet) is in this tab.
Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: A Systematic Review and Framework for Safe Adoption
ABSTRACT
Background:
Artificial intelligence (AI) studies show promise in improving accuracy and efficiency in mammographic screening programs worldwide. However, its adoption in the clinical workflow faces challenges, such as unintended errors, professional training needs, and ethical concerns. Of note, specific frameworks for AI in breast cancer screening are lacking.
Objective:
This paper reports a systematic review aiming to assess existing literature and develop a tailored AI governance framework for adoption in breast cancer screening
Methods:
Three electronic databases (PubMed, EMBASE, and Medline) were searched using combinations of the key words, “artificial intelligence”, “regulation”, “governance”, “breast cancer” and “screening”. Original studies evaluating AI in breast cancer detection or discussing challenges related to AI implementation in this setting were eligible for review. Findings were narratively synthesized before being mapped directly against the constructs within the Consolidated Framework for Implementation Research (CFIR).
Results:
A total of 1240 results were retrieved, and 20 original studies were eventually included in this systematic review. Studies identified challenges in adopting AI in breast screening included reproducibility, evidentiary standards, technology concerns, trust issues, ethical, legal, societal concerns, and post-adoption uncertainty. Mapping these findings against the constructs within the CFIR, we recognize the complex interactions in the development and implementation of a governance framework, across the AI adoption life-cycle in the context of breast cancer screening. Action plans corresponding to the main challenges were included within the framework, aiding in a structured approach to address these issues.
Conclusions:
This systematic review identified key themes as well as the barriers and facilitators for AI governance in breast cancer screening. Post-market surveillance is emphasized for continuous monitoring and auditing to ensure the effectiveness and ethical implementation of AI in breast cancer screening. Clinical Trial: na
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.